You can deploy this model and start earning money today!

manoh2f2/
recommend_songs

Name: recommend_songs
Availability: InStock

Views

No views yet

Para un uso sencillo del modelo utilize el siguiente codigo:

 #! pip install transformers
 #! pip install torch
 #! pip install datasets

from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from datasets import load_dataset
import numpy as np
import torch


dataset = load_dataset("manoh2f2/songs_resampled")

# Cargar el dataset en un DataFrame
split_name = 'train'
df_resampled = dataset[split_name].to_pandas()

tokenizer = AutoTokenizer.from_pretrained("manoh2f2/recommend_songs")
model = AutoModelForSequenceClassification.from_pretrained("manoh2f2/recommend_songs")

# Define a prompt
prompt = "I am happy"

# Tokenize the prompt
encoded_prompt = tokenizer(prompt, return_tensors='pt', max_length=256)

# Make a prediction using the trained model
with torch.no_grad():
    model_output = model(**encoded_prompt)

# Get the predicted emotion index
predicted_emotion_index = torch.argmax(model_output.logits).item()

# Map the index back to the emotion label using the DataFrame
predicted_emotion_label = df_resampled['emotions'].unique()[predicted_emotion_index]

# Get a song associated with the predicted emotion from the DaraFrame
result = df_resampled[df_resampled['emotions'] == predicted_emotion_label]

# Get the number of rows in the DataFrame
num_rows = result.shape[0]
#Generate a random index to select a random song from the DataFrame
random_index = np.random.randint(0, num_rows)

#Get the recommended song and artist
recommended_song = result['song'].iloc[random_index]
recommended_artist = result['artist'].iloc[random_index]

#Print the results
print(f"Prompt: {prompt}")
print(f"Predicted Emotion: {predicted_emotion_label}")
print(f"Recommended Song: {recommended_song} - {recommended_artist}")